The name is absent

functions, from basic survival responses to higher-level cognitive functions such as
memory and emotion (Kandel et al., 2000). However, changes to this order, whether
they arise from trauma or from disease, can have life-changing consequences ranging
from tremors (as with Parkinson’s disease) (Kandel et al., 2000), sudden seizures
(as with epilepsy) (Traub and Miles, 1991), or the inability to form new memories
(Johnston and Amaral, 1998). In order to diagnose and treat such disorders, it is
necessary to understand how the brain works from the small scale of single neurons
to the large scale of functional regions.

For example, the hippocampus is a cortical structure that is of critical importance
in the formation and storage of memories (Traub and Miles, 1991) as well as in spatial
navigation tasks (Witter and Moser, 2006) (Yoganarasimha et al., 2006). Multiple
hippocampal regions, each with their own distinct cell types, are involved in these cog-
nitive functions (Ahmed and Mehta, 2009). In order to realize a multi-scale model,
one must know the density of connections between these regions, the prototypical
morphology and kinetics of each cell type, and the spatio-temporal structure of the
inputs to each region. Experimental studies have provided insight into each of these
features, thereby enhancing the biophysical accuracy of the resulting models (Koch,
1999). However, having proper initial data is only part of the story; actual simu-
lations allow investigators to validate experimental data and explore new questions.
To achieve this, the tools of computational neuroscience must be able to deal with
problems on the massive scales described above, which has proven to be a challenge.

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